Computer Vision 1 (Winter Term 2026/2027)

Overview

• Course consisting of:
  • Lectures (2 hours/week) in HSZ/AUDI/H on Mondays, 11:10–12:40
  • Exercise group sessions (2 hours/week):
    • SCH/A251, Tuesdays, 13:00–14:30, and alternatively:
    • HSZ/0002/E, Thursdays, 13:00–14:30
  • Self-study
  • Final examination
• Language (course and examination): English
• Registration (OPAL). Additional rules for course registration may apply, depending on the study program.
• Lecturer: Bjoern Andres
• Teaching assistant: David Stein

Contents

• Introduction
• Mathematical foundations
  • Linear and integer optimization for computer vision
• Operators on digital images
  • Digital images
  • Point operators
  • Linear operators, including convolution
  • Non-linear operators, including edge and corner detection
• Machine learning for computer vision
  • Logistic regression
  • Deep learning for computer vision
  • Attention and transformers for computer vision
• Classification of digital images
  • Classification of digital images and image patches
  • Supervised learning problems and inference problems
• Segmentation of digital images
  • Image segmentation
  • Semantic segmentation
• Object recognition in digital images
  • Single object recognition
  • Multiple object recognition
• Object tracking in digital images
  • Single object tracking
  • Multiple object tracking
• Embedding of digital images
  • Principal component analysis
  • Auto-encoders
  • Variational auto-encoders